PaddleX/paddlex/modules/table_recognition/trainer.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import os
from pathlib import Path
import lazy_paddle as paddle

from ..base import BaseTrainer
from ...utils.config import AttrDict
from .model_list import MODELS


class TableRecTrainer(BaseTrainer):
    """Table Recognition Model Trainer"""

    entities = MODELS

    def update_config(self):
        """update training config"""
        if self.train_config.log_interval:
            self.pdx_config.update_log_interval(self.train_config.log_interval)
        if self.train_config.eval_interval:
            self.pdx_config._update_eval_interval_by_epoch(
                self.train_config.eval_interval
            )
        if self.train_config.save_interval:
            self.pdx_config.update_save_interval(self.train_config.save_interval)

        self.pdx_config.update_dataset(
            self.global_config.dataset_dir, "PubTabTableRecDataset"
        )
        if self.train_config.pretrain_weight_path:
            self.pdx_config.update_pretrained_weights(
                self.train_config.pretrain_weight_path
            )
        if self.train_config.batch_size is not None:
            self.pdx_config.update_batch_size(self.train_config.batch_size)
        if self.train_config.learning_rate is not None:
            self.pdx_config.update_learning_rate(self.train_config.learning_rate)
        if self.train_config.epochs_iters is not None:
            self.pdx_config._update_epochs(self.train_config.epochs_iters)
        if (
            self.train_config.resume_path is not None
            and self.train_config.resume_path != ""
        ):
            self.pdx_config._update_checkpoints(self.train_config.resume_path)
        if self.global_config.output is not None:
            self.pdx_config._update_output_dir(self.global_config.output)

    def get_train_kwargs(self) -> dict:
        """get key-value arguments of model training function

        Returns:
            dict: the arguments of training function.
        """
        return {
            "device": self.get_device(),
            "dy2st": self.train_config.get("dy2st", False),
        }